An Efficient Probabilistic Algorithm to Detect Periodic Patterns in Spatio-Temporal Datasets

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

1. Page 1 -Results and Contributions: The outcomes of the proposed new algorithm and its comparison with existing ones are not summarized. Adding a sentence about the main findings (e.g., performance metrics, efficiency) and their implications for the field would strengthen the abstract.

Page 9-5.1. Experimental Environments 

2.Clarity and Detail in Dataset Description:It would be helpful to include more specifics about the synthetic dataset—how it was generated, the characteristics of the data, and why these particular characteristics were chosen. 

3. For the Geolife dataset, a more detailed explanation of how the grid was defined and the rationale behind the chosen grid size could enhance understanding of the experimental setup.

4. Experimental Setup:

Clarification on how the grid and timestamp data representation affects the performance and applicability of the algorithms would be valuable. How does this representation compare to other possible methods?

5. The representation of motion as a sequence of characters is interesting but needs more justification. Why is this approach chosen, and how does it impact the accuracy and efficiency of pattern detection?

6. Page 10:Merging tables 2 through 19 into several single tables on page 10 is a logical step given the consistency in terms, metrics, and abbreviations across them. 

Page 16:Conclusions and Future Work

7. Future Work:The section could be strengthened by a more detailed discussion of potential future work. This might include specific directions for further research based on the findings, such as potential improvements to the algorithms that showed weaker performance or the exploration of different data sets to validate and generalize the results.

8. It would be beneficial to discuss the practical implications of your findings. How might these algorithms be applied in real-world scenarios? What are the potential benefits or challenges in implementing these algorithms in different contexts?

9.A discussion of any technical limitations encountered during the research and how they might be addressed in future studies would provide a more balanced view. This could include limitations related to data set size, computational resources, or algorithmic scalability.

10. The conclusion could be enhanced by integrating a broader perspective on the field. How do your findings fit within the current landscape of research in pattern mining and data analysis? 

Comments on the Quality of English Language

The manuscript is generally well-written with a clear structure and articulate presentation of complex concepts.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The article presents a new probabilistic algorithm called Minus-F1 for detecting periodic patterns in spatio-temporal data. This algorithm addresses the challenge of the data deluge by improving processing efficiency for large volumes of data where traditional data mining techniques fall short. The authors highlight the necessity of such advanced algorithms as data grows faster than the capacity of existing computational power. The Minus-F1 algorithm leverages probabilistic techniques to enhance performance and accuracy in identifying periodic patterns, which are crucial for timely decision-making based on data insights. The paper conducts a thorough empirical comparison of Minus-F1 against well-cited algorithms like Apriori, MS-Apriori, FP-Growth, Max-Subpattern, and PPA, using two distinct datasets—one synthetic and one real. Results indicate that the probabilistic Minus-F1 outperforms these traditional algorithms in both speed and efficiency. The study highlights the algorithm's robustness in various testing scenarios, demonstrating its potential to significantly improve pattern detection in large-scale data environments. This contribution is pivotal for fields requiring rapid and reliable data analysis, such as dynamic geographic information systems and location-based services. Future work suggested by the authors involves extending the algorithm’s capabilities and further optimizing its computational efficiency. Overall, the Minus-F1 algorithm represents a significant advancement in the field of data mining, particularly for applications involving complex spatio-temporal data.

I think the paper should be accepted.

Author Response

Dear Reviewer: Thank you for taking the time to read our manuscript. We sincerely appreciate your views. We are delighted to hear that you think our work should be accepted.

 

Reviewer 3 Report

Comments and Suggestions for Authors

 

This article presents a probabilistic version of a previously developed algorithm, MINUS-F1 for finding periodic patterns in large datasets. The performance of the probabilistic algorithm is assessed empirically using synthetic and Geolife dataset, against various algorithms including MINUS-F1 in terms of processing time and pattern discovery.

As for me, I am uncertain that this article presents a significant contribution due to the following flaws:

·        The article lacks a clear discussion on the limitations of the existing deterministic algorithm. Identifying these limitations would help establish the need for the probabilistic algorithm.

·        The impact of input distribution on the deterministic algorithm's, Minus-F1, performance is not explored. It would be beneficial to demonstrate how different input distributions might affect the performance of Minus-F1.

·        A crucial missing aspect is a formal proof of correctness for the proposed probabilistic Minus-F1.

 

·        Although the probabilistic Minus-F1 maintains the same running time complexity Θ(m.n^2) as Minus-F1, additional benefits beyond just time complexity are required, such as improved efficiency in specific cases, reduced memory usage, enhanced accuracy for uncertain data, or approximation guarantees

 

 

Author Response

Dear Reviewer: Thank you for taking the time to read our manuscript. We are grateful for your comments. We have improved our text by following the advice of the Reviewers to make changes and corrections. As suggested by one of the Reviewers, we have stated our contributions at the end of the Introduction and amended the Abstract. As indicated by another Reviewer, the description of the datasets used in our experiments is more detailed now, and our results discussed in a separate section.

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

After reviewing the revisions made by the authors in response to my previous comments, I find that the manuscript is now well-structured. I recommend accepting the paper for publication.

Comments on the Quality of English Language

The quality of the English language in this manuscript is generally good. However, I recommend a minor review by a professional editor to refine grammar and syntax where necessary. 

Author Response

Dear Reviewer,

Thank you for taking the time to read our manuscript for the second time and consider our amendments and extensions. We are delighted to hear that you think that our submission should be accepted for publication.

Thank you for agreeing that the quality of the English language in our manuscript is generally good. We have continued to improve the contents by fixing a couple of typos that we were able to discover while addressing the comments of another reviewer. We believe that the latest changes have also enhanced our submission.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

First of all, I would like to thank the authors for the efforts they made to improve the manuscript. However, unfortunately in the second version of the mauscript, I have not found direct responses to the comments and concerns I raised on the first version of the manuscript. This lack of point-to-poin responses to my previous comments has made it challenging for me to evaluate the revisions comprehensively. As a result, I find myself in a similar position to my evaluation of the first version.

 

Author Response

Please see the attachment. Thank you!

Author Response File: Author Response.pdf

Round 3

Reviewer 3 Report

Comments and Suggestions for Authors

The authors have addressed my previous comments adequately. Thank you